Fuzzy Quotients in Reactive Common Sense Reasoning

In contemporary distributed applications questions concerning coordination have become increasingly urgent. There is a trade-off however to be made between the need for a highly reactive behavior and the need for semantically rich high level abstractions. Especially w.r.t. context-aware applications where various systems have to act together and come to coordinated conclusions the need for powerful semantic abstractions is evident. Our research is based on the observation that human teams are very good in coordinating (when compared to technical systems). Consequently we chose an approach of common sense reasoning which is capable to grasp the specifics of human behavior. One specific in this approach is the usage of fuzzy quotients which bears strong similarities to the notion of granules.

[1]  H. Lorenz,et al.  Multilineage cells from human adipose tissue: implications for cell-based therapies. , 2001, Tissue engineering.

[2]  C Krettek,et al.  Comparison of human bone marrow stromal cells seeded on calcium-deficient hydroxyapatite, beta-tricalcium phosphate and demineralized bone matrix. , 2003, Biomaterials.

[3]  N Passuti,et al.  Osteogenic potential in vitro of human bone marrow cells cultured on macroporous biphasic calcium phosphate ceramic. , 1999, Journal of biomedical materials research.

[4]  Stuart C. Shapiro Review of Knowledge representation: logical, philosophical, and computational foundations by John F. Sowa. Brooks/Cole 2000. , 2001 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[6]  Daniel Le Métayer,et al.  Programming by multiset transformation , 1993, CACM.

[7]  Y. Hata,et al.  A Fuzzy Estimation System for Cellular Quantity of Artificial Culture Bone , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[8]  Wolfgang Grieskamp,et al.  From program languages to software languages , 2002, J. Syst. Softw..

[9]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[10]  Steffen Hölldobler,et al.  The Fuzzy Description Logic ALCFH with Hedge Algebras as Concept Modifiers , 2003, J. Adv. Comput. Intell. Intell. Informatics.

[11]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[12]  S. Ichinose,et al.  Self-organization mechanism in a bone-like hydroxyapatite/collagen nanocomposite synthesized in vitro and its biological reaction in vivo. , 2001, Biomaterials.

[13]  J. McCarthy,et al.  Formalizing Context (Expanded Notes) , 1994 .

[14]  Jon Barwise,et al.  Information Flow: The Logic of Distributed Systems , 1997 .

[15]  Gheorghe Paun,et al.  Introduction to Membrane Computing , 2006, Applications of Membrane Computing.

[16]  Umberto Straccia,et al.  Reasoning within Fuzzy Description Logics , 2011, J. Artif. Intell. Res..

[17]  Werner Nutt,et al.  Basic Description Logics , 2003, Description Logic Handbook.

[18]  Lester W. Schmerr,et al.  FUNDAMENTAL MODELS AND MEASUREMENTS FOR ULTRASONIC NONDESTRUCTIVE EVALUATION SYSTEMS , 2007 .

[19]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[20]  Apostolos Syropoulos Fuzzifying P Systems , 2006, Comput. J..

[21]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[22]  Arthur B. Markman,et al.  Knowledge Representation , 1998 .